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As more complex predictive models are used for gamma-ray spectral analysis, methods are needed to probe and understand their predictions and behavior. Recent work has begun to bring the latest techniques from the field of Explainable Artificial Intelligence (XAI) into the applications of gamma-ray spectroscopy, including the introduction of gradient-based methods like saliency mapping and Gradient-weighted Class Activation Mapping (Grad-CAM), and black box methods like Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). In addition, new sources of synthetic radiological data are becoming available, and these new data sets present opportunities to train models using more data than ever before. In this work, we use a neural network model trained on synthetic NaI(Tl) urban search data to compare some of these explanation methods and identify modifications that need to be applied to adapt the methods to gamma-ray spectral data. We find that the black box methods LIME and SHAP are especially accurate in their results, and recommend SHAP since it requires little hyperparameter tuning. We also propose and demonstrate a technique for generating counterfactual explanations using orthogonal projections of LIME and SHAP explanations. [ABSTRACT FROM AUTHOR]

We present an experimental demonstration of additive point source localization (APSL), a sparse parametric imaging algorithm that reconstructs the 3-D positions and activities of multiple gamma-ray point sources. Using a handheld gamma-ray detector array and up to four 8 μ Ci 137Cs gamma-ray sources, we performed both source-search and source-separation experiments in an indoor laboratory environment. In the majority of the source-search measurements, APSL reconstructed the correct number of sources with position accuracies of ~20 cm and activity accuracies (unsigned) of ~20%, given measurement times of 2 to 3 min and distances of closest approach (to any source) of ~20 cm. In source-separation measurements where the detector could be moved freely about the environment, APSL was able to resolve two sources separated by 75 cm or more given only ~60 s of measurement time. In these source-separation measurements, APSL produced larger total activity errors of ~40%, but obtained source-separation distances accurate to within 15 cm. We also compare our APSL results against traditional maximum likelihood-expectation maximization (ML-EM) reconstructions and demonstrate improved image accuracy and interpretability using APSL over ML-EM. These results indicate that APSL is capable of accurately reconstructing gamma-ray source positions and activities using measurements from existing detector hardware. [ABSTRACT FROM AUTHOR]

Gamma-ray imaging attempts to reconstruct the spatial and intensity distribution of gamma-emitting radionuclides from a set of measurements. Generally, this problem is solved by discretizing the spatial dimensions and employing the maximum likelihood expectation maximization (ML-EM) algorithm, with or without some form of regularization. While the generality of this formulation enables use in a wide variety of scenarios, it is susceptible to overfitting, limited by the discretization of spatial coordinates, and can be computationally expensive. We present a novel approach to 3D gamma-ray image reconstruction for scenarios where sparsity may be assumed, for example, radiological source search. In this paper, we first formulate a point-source localization (PSL) approach as an optimization problem, where both position and source intensity are continuous variables. We then extend and generalize this formulation to an iterative algorithm, called additive PSL (APSL), for sparse parametric image reconstruction. A set of simulated source search scenarios using a single non-directional detector are considered, finding improved image accuracy and computational efficiency with APSL over traditional grid-based approaches. [ABSTRACT FROM AUTHOR]

The Airborne Radiological Enhanced-sensor System (ARES) includes a prototype helicopter-borne CsI(Na) detector array that has been developed as part of the DHS Domestic Nuclear Detection Office Advanced Technology Demonstration. The detector system geometry comprises two pairs of 23-detector arrays designed to function as active masks, providing additional angular resolution of measured gamma rays in the roll dimension. Experimental measurements, using five radioisotopes (137Cs, 60Co, 241Am, 131I, and ^99m Tc), were performed to map the detector response in both roll and pitch dimensions. This paper describes the acquisition and analysis of these characterization measurements, calculation of the angular response of the ARES system, and how this response function is used to improve aerial detection and localization of radiological and nuclear threat sources. [ABSTRACT FROM PUBLISHER]

Researchers at Lawrence Berkeley National Laboratory have been supporting the Transformational and Applied Research Directorate in the Domestic Nuclear Detection Office of the Department of Homeland Security to define needs for, to develop, and to test a scintillator-based radiation detection and localization system to be fielded on a helicopter platform - the so-called Airborne Radiological Enhanced-sensor System. The system comprises an array of 92 CsI(Na) detectors that are arranged to function as an active mask to encode the directionality in the roll-dimension of measured gamma rays and is additionally capable of Compton imaging. Additional contextual sensors and specially-developed algorithms are also being fielded for characterization with the goal of detecting, localizing, and helping to interdict radiological and nuclear threats via airborne search. The algorithms that are being developed leverage contextual information including topography, geography, hyperspectral imagery, video tracking, and platform positioning. This paper describes recent characterization efforts of the CsI(Na) detector system including energy, position, and timing resolution and synchronization between the 184 individual photomultiplier tubes. [ABSTRACT FROM AUTHOR]

The enormous advances in sensing and data processing technologies in combination with recent developments in nuclear radiation detection and imaging enable unprecedented and "smarter" ways to detect, map, and visualize nuclear radiation. The recently developed concept of three-dimensional (3-D) Scene-data fusion allows us now to "see" nuclear radiation in three dimensions, in real time, and specific to radionuclides. It is based on a multi-sensor instrument that is able to map a local scene and to fuse the scene data with nuclear radiation data in 3-D while the instrument is freely moving through the scene. This new concept is agnostic of the deployment platform and the specific radiation detection or imaging modality. We have demonstrated this 3-D Scene-data fusion concept in a range of configurations in locations, such as the Fukushima Prefecture in Japan or Chernobyl in Ukraine on unmanned and manned aerial and ground-based platforms. It provides new means in the detection, mapping, and visualization of radiological and nuclear materials relevant for the safe and secure operation of nuclear and radiological facilities or in the response to accidental or intentional releases of radioactive materials where a timely, accurate, and effective assessment is critical. In addition, the ability to visualize nuclear radiation in 3-D and in real time provides new means in the communication with public and facilitates to overcome one of the major public concerns of not being able to "see" nuclear radiation.